Statistics

ISBN-10: 0596510497
ISBN-13: 9780596510497
Edition: 2008 (Revised)
List price: $34.99
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Description: Need to learn statistics as part of your job, or want some help passing a statistics course? Statistics in a Nutshell is a clear and concise introduction and reference that's perfect for anyone with no previous background in the subject. This book  More...

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Book details

List price: $34.99
Copyright year: 2008
Publisher: O'Reilly Media, Incorporated
Publication date: 8/4/2008
Binding: Paperback
Pages: 452
Size: 5.75" wide x 9.00" long x 1.25" tall
Weight: 1.386
Language: English

Need to learn statistics as part of your job, or want some help passing a statistics course? Statistics in a Nutshell is a clear and concise introduction and reference that's perfect for anyone with no previous background in the subject. This book gives you a solid understanding of statistics without being too simple, yet without the numbing complexity of most college texts. You get a firm grasp of the fundamentals and a hands-on understanding of how to apply them before moving on to the more advanced material that follows. Each chapter presents you with easy-to-follow descriptions illustrated by graphics, formulas, and plenty of solved examples. Before you know it, you'll learn to apply statistical reasoning and statistical techniques, from basic concepts of probability and hypothesis testing to multivariate analysis. Organized into four distinct sections, Statistics in a Nutshell offers you: Introductory material: Different ways to think about statistics Basic concepts of measurement and probability theory Data management for statistical analysis Research design and experimental design How to critique statistics presented by others Basic inferential statistics: Basic concepts of inferential statistics The concept of correlation, when it is and is not an appropriate measure of association Dichotomous and categorical data The distinction between parametric and nonparametric statistics Advanced inferential techniques: The General Linear Model Analysis of Variance (ANOVA) and MANOVA Multiple linear regression Specialized techniques: Business and quality improvement statistics Medical and public health statistics Educational and psychological statistics Unlike many introductory books on the subject, Statistics in a Nutshell doesn't omit important material in an effort to dumb it down. And this book is far more practical than most college texts, which tend to over-emphasize calculation without teaching you when and how to apply different statistical tests. With Statistics in a Nutshell , you learn how to perform most common statistical analyses, and understand statistical techniques presented in research articles. If you need to know how to use a wide range of statistical techniques without getting in over your head, this is the book you want.

Sarah Boslaugh, Ph.D. has more than 20 years of experience working in data management and statistical analysis. She has worked as an SPSS programmer in many different settings in the public and private spheres, including academia, health care, government, and the insurance industry. Dr. Boslaugh received her Ph.D. in research methods and evaluation from the City University of New York and is currently a Senior Statistical Data Analyst at the Washington University School of Medicine in St. Louis. Her research interests include multilevel modeling, geographic information systems, and measurement theory.

Paul A. Watters PhD CITP is Head of Data Services at the Medical Research Council's National Survey of Health and Development, which is the oldest of the British birth cohort studies. He is also an honorary senior research fellow at University College London. Dr. Watters is the project manager for the MRC's Data Access Project, and is presently investigating methods for securing investigator access to public health data in large-scale distributed systems in a challenging ethical and legal environment. He has an active research interest in the use of orthogonal and non-orthogonal methods for feature extraction in pattern recognition, especially in biometric applications.

Preface
Basic Concepts of Measurement
Measurement
Levels of Measurement
True and Error Scores
Reliability and Validity
Measurement Bias
Exercises
Probability
About Formulas
Basic Definitions
Defining Probability
Bayes's Theorem
Enough Exposition, Let's Do Some Statistics!
Exercises
Data Management
An Approach, Not a Set of Recipes
The Chain of Command
Codebooks
The Rectangular Data File
Spreadsheets and Relational Databases
Inspecting a New Data File
String and Numeric Data
Missing Data
Descriptive Statistics and Graphics
Populations and Samples
Measures of Central Tendency
Measures of Dispersion
Outliers
Graphic Methods
Bar Charts
Bivariate Charts
Exercises
Research Design
Observational Studies
Experimental Studies
Gathering Experimental Data
Inference and Threats to Validity
Eliminating Bias
Example Experimental Design
Critiquing Statistics Presented by Others
The Misuse of Statistics
Common Problems
Quick Checklist
Research Design
Descriptive Statistics
Inferential Statistics
Inferential Statistics
Probability Distributions
Independent and Dependent Variables
Populations and Samples
The Central Limit Theorem
Hypothesis Testing
Confidence Intervals
p-values
Data Transformations
Exercises
The t-Test
The t Distribution
t-Tests
One-Sample t-Test
Two-Sample t-Test
Repeated Measures t-Test
Unequal Variance t-Test
Effect Size and Power
Exercises
The Correlation Coefficient
Measuring Association
Graphing Associations Through Scatterplots
Pearson's Product-Moment Correlation Coefficient
Coefficient of Determination
Spearman Rank-Order Coefficient
Advanced Techniques
Categorical Data
The R x C Table
The Chi-Square Distribution
The Chi-Square Test
Fisher's Exact Test
McNemar's Test for Matched Pairs
Correlation Statistics for Categorical Data
The Likert and Semantic Differential Scales
Exercises
Nonparametric Statistics
Nonnormal Data
Between Subjects Designs
Within-Subjects Designs
Exercises
Introduction to the General Linear Model
The General Linear Model
Linear Regression
Analysis of Variance (ANOVA)
Exercises
Extensions of Analysis of Variance
Factorial ANOVA
MANOVA
ANCOVA
Repeated Measures ANOVA
Mixed Designs
Multiple Linear Regression
Multiple Regression Models
Common Problems with Multiple Regression
Exercises
Other Types of Regression
Logistic Regression
Logarithmic Transformations
Polynomial Regression
Overfitting
Other Statistical Techniques
Factor Analysis
Cluster Analysis
Discriminant Function Analysis
Multidimensional Scaling
Business and Quality Improvement Statistics
Index Numbers
Time Series
Decision Analysis
Quality Improvement
Exercises
Medical and Epidemiological Statistics
Measures of Disease Frequency
Ratio, Proportion, and Rate
Prevalence and Incidence
Crude, Category-Specific, and Standardized Rates
The Risk Ratio
The Odds Ratio
Confounding, Stratified Analysis, and the Mantel-Haenszel Common Odds Ratio
Power Analysis
Sample Size Calculations
Exercises
Educational and Psychological Statistics
Percentiles
Standardized Scores
Test Construction
Classical Test Theory: The True Score Model
Reliability of a Composite Test
Measures of Internal Consistency
Item Analysis
Item Response Theory
Exercises
Review of Basic Mathematics
Introduction to Statistical Packages
References
Index

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